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Can Lung Imaging Scores and Clinical Variables Predict Severe Course and Fatal Outcome in COVID-19 Pneumonia Patients? A Single-Center Observational Study
COVID-19 prediction models mostly consist of combined clinical features, laboratory parameters, and, less often, chest X-ray (CXR) findings. Our main goal was to propose a prediction model involving imaging methods, specifically ultrasound. This was a single-center, retrospective cohort observationa...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147733/ https://www.ncbi.nlm.nih.gov/pubmed/35629402 http://dx.doi.org/10.3390/life12050735 |
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author | Skopljanac, Ivan Pavicic Ivelja, Mirela Budimir Mrsic, Danijela Barcot, Ognjen Jelicic, Irena Domjanovic, Josipa Dolic, Kresimir |
author_facet | Skopljanac, Ivan Pavicic Ivelja, Mirela Budimir Mrsic, Danijela Barcot, Ognjen Jelicic, Irena Domjanovic, Josipa Dolic, Kresimir |
author_sort | Skopljanac, Ivan |
collection | PubMed |
description | COVID-19 prediction models mostly consist of combined clinical features, laboratory parameters, and, less often, chest X-ray (CXR) findings. Our main goal was to propose a prediction model involving imaging methods, specifically ultrasound. This was a single-center, retrospective cohort observational study of patients admitted to the University Hospital Split from November 2020 to May 2021. Imaging protocols were based on the assessment of 14 lung zones for both lung ultrasound (LUS) and computed tomography (CT), correlated to a CXR score assessing 6 lung zones. Prediction models for the necessity of mechanical ventilation (MV) or a lethal outcome were developed by combining imaging, biometric, and biochemical parameters. A total of 255 patients with COVID-19 pneumonia were included in the study. Four independent predictors were added to the regression model for the necessity of MV: LUS score, day of the illness, leukocyte count, and cardiovascular disease (χ2 = 29.16, p < 0.001). The model accurately classified 89.9% of cases. For the lethal outcome, only two independent predictors contributed to the regression model: LUS score and patient’s age (χ2 = 48.56, p < 0.001, 93.2% correctly classified). The predictive model identified four key parameters at patient admission which could predict an adverse outcome. |
format | Online Article Text |
id | pubmed-9147733 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91477332022-05-29 Can Lung Imaging Scores and Clinical Variables Predict Severe Course and Fatal Outcome in COVID-19 Pneumonia Patients? A Single-Center Observational Study Skopljanac, Ivan Pavicic Ivelja, Mirela Budimir Mrsic, Danijela Barcot, Ognjen Jelicic, Irena Domjanovic, Josipa Dolic, Kresimir Life (Basel) Article COVID-19 prediction models mostly consist of combined clinical features, laboratory parameters, and, less often, chest X-ray (CXR) findings. Our main goal was to propose a prediction model involving imaging methods, specifically ultrasound. This was a single-center, retrospective cohort observational study of patients admitted to the University Hospital Split from November 2020 to May 2021. Imaging protocols were based on the assessment of 14 lung zones for both lung ultrasound (LUS) and computed tomography (CT), correlated to a CXR score assessing 6 lung zones. Prediction models for the necessity of mechanical ventilation (MV) or a lethal outcome were developed by combining imaging, biometric, and biochemical parameters. A total of 255 patients with COVID-19 pneumonia were included in the study. Four independent predictors were added to the regression model for the necessity of MV: LUS score, day of the illness, leukocyte count, and cardiovascular disease (χ2 = 29.16, p < 0.001). The model accurately classified 89.9% of cases. For the lethal outcome, only two independent predictors contributed to the regression model: LUS score and patient’s age (χ2 = 48.56, p < 0.001, 93.2% correctly classified). The predictive model identified four key parameters at patient admission which could predict an adverse outcome. MDPI 2022-05-15 /pmc/articles/PMC9147733/ /pubmed/35629402 http://dx.doi.org/10.3390/life12050735 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Skopljanac, Ivan Pavicic Ivelja, Mirela Budimir Mrsic, Danijela Barcot, Ognjen Jelicic, Irena Domjanovic, Josipa Dolic, Kresimir Can Lung Imaging Scores and Clinical Variables Predict Severe Course and Fatal Outcome in COVID-19 Pneumonia Patients? A Single-Center Observational Study |
title | Can Lung Imaging Scores and Clinical Variables Predict Severe Course and Fatal Outcome in COVID-19 Pneumonia Patients? A Single-Center Observational Study |
title_full | Can Lung Imaging Scores and Clinical Variables Predict Severe Course and Fatal Outcome in COVID-19 Pneumonia Patients? A Single-Center Observational Study |
title_fullStr | Can Lung Imaging Scores and Clinical Variables Predict Severe Course and Fatal Outcome in COVID-19 Pneumonia Patients? A Single-Center Observational Study |
title_full_unstemmed | Can Lung Imaging Scores and Clinical Variables Predict Severe Course and Fatal Outcome in COVID-19 Pneumonia Patients? A Single-Center Observational Study |
title_short | Can Lung Imaging Scores and Clinical Variables Predict Severe Course and Fatal Outcome in COVID-19 Pneumonia Patients? A Single-Center Observational Study |
title_sort | can lung imaging scores and clinical variables predict severe course and fatal outcome in covid-19 pneumonia patients? a single-center observational study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147733/ https://www.ncbi.nlm.nih.gov/pubmed/35629402 http://dx.doi.org/10.3390/life12050735 |
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